A Brain Computer Interface (BCI) system takes and classifies a user’s brain activity into a signal to which a computer can respond. To control a BCI, the user should produce various brain activity patterns which are captured in form of Electroencephalogram (EEG) and converted to commands by identifying the patterns by the system. Such classification was undertaken by various methods, and perfor...

The majority of subjects who attempt to learn control of a brain-computer interface (BCI) can do so with adequate training. Much like when one learns to type or ride a bicycle, BCI users report transitioning from a deliberate, cognitively focused mindset to near automatic control as training progresses. What are the neural correlates of this process of BCI skill acquisition? Seven subjects were...

This thesis describes an SSVEP-based BCI implemented as a practical part of this work. One possible usage of a BCI that efficiently implements a communication channel between the brain and an external device would be to help severely disabled people to control devices that currently require pushing buttons, for example an electric wheelchair. The BCI implemented as a part of this thesis uses wi...

Trauma patients with thoracic aortic injury (TAI) suffer blunt cardiac injury (BCI) at variable frequencies. This investigation aimed to determine the frequency of BCI in trauma patients with TAI and compare with those without TAI. All trauma patients with TAI who had admission electrocardiography (ECG) and serum creatine kinase-MB (CK-MB) from January 1999 to May 2009 were included as a study ...

The aim of this paper is to introduce the notions of ($epsilon, epsilon vee q$)-fuzzy p-ideals, ($epsilon, epsilon vee q$)-fuzzy q-ideals and ($epsilon, epsilon vee q$)-fuzzy a-ideals in BCIalgebras and to investigate some of their properties. Several characterizationtheorems for these generalized fuzzy ideals are proved and the relationshipamong these generalized fuzzy ideals of BCI-algebras i...

In this study, a novel control algorithm, based on a P300-based brain-computer interface (BCI) is fully developed to control a 2-DoF robotic arm. Eight subjects including 5 men and 3 women perform a 2-dimensional target tracking in a simulated environment. Their EEG (Electroencephalography) signals from visual cortex are recorded and P300 components are extracted and evaluated to perform a real...

The present study aims to gain insights into the effects of training with a motor imagery (MI)-based brain-computer interface (BCI) on activation patterns of the sensorimotor cortex. We used functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to investigate long-term training effects across 10 sessions using a 2-class (right hand and feet) MI-based BCI in fifteen subj...